Object detection apparatus, object detection method and object detection program

ABSTRACT

An object detection apparatus and method for accurately detecting a movable object in a region around a vehicle from a time series of images obtained through a camera mounted on the vehicle by eliminating the influence of the movement of the camera through simple processing, and a program for making a computer execute processing in the apparatus. The object detection apparatus has a feature point extraction unit which extracts a feature point contained in a feature region of each image in the time series of images obtained through a camera mounted on the vehicle, a correspondence degree computation unit which computes the degree of correspondence for each pair of the feature points, wherein one of the feature points in the each pair is each of one or more of the feature points extracted by the feature point extraction unit from one of two images taken by the camera at different times, and another of the feature points in the each pair is each of a plurality of the feature points extracted by the feature point extraction unit from another of the two images, and a detection unit which detects the movable object on the basis of the degree of correspondence computed by the correspondence degree computation unit.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to an object detection apparatus andmethod for detecting a movable object existing in a region around avehicle from images obtained through a camera mounted on the vehicle,and an object detection program for making a computer execute processingin the object detection apparatus.

2. Description of the Related Art

In recent years, techniques to detect an object approaching a vehicle bymonitoring the surroundings of the vehicle and to perform operationssuch as presentation of information to the driver and control of travelof the vehicle on the basis of the detection results have been proposed.As a method of detecting an object, techniques are known in which imagesof a region surrounding a vehicle are taken with a camera mounted on thevehicle and the taken images are processed to detect a movable object.As one of such techniques, a technique using an optical flow computedfrom a time series of images obtained through a single camera can bementioned. An optical flow is a vector indicating an apparent motion onan image detected from a plurality of images taken at different times.In the case of taking images with a camera mounted on a vehicle, thecamera itself moves and, therefore, an apparent motion is produced onthe images by the movement of the camera. For this reason, a techniquein which the amount of movement of a camera itself is estimated indetection of a movable object by using an optical flow, and the movableobject is suitably detected by eliminating the influence of the movementof the camera has been proposed (see, for example, Japanese PatentLaid-Open No. 2004-56763 (hereinafter referred to as Patent Document1)).

A monitoring apparatus for use in a vehicle disclosed in Patent Document1 has an optical flow detection unit which obtains an optical flow fromimages taken with a camera, a background flow estimation unit whichobtains a background flow, i.e., a camera image flow (a flow produced bythe movement of the camera itself) and an approaching object detectionunit which detects movement of an object existing in a region around thevehicle by comparing the optical flow and the background flow. Thebackground flow estimation unit obtains the background flow on the basisof the movement of the vehicle estimated by a vehicle movementestimation unit and a spatial model estimated by a spatial modelestimation unit (a model for the space from which images are taken bythe camera).

In the apparatus disclosed in Patent Document 1, however, the vehiclemovement estimation unit estimates the movement of the vehicle using themonitoring apparatus on the basis of the wheel speed, steering angle andother factors, and obtains the background flow from the movement of thevehicle. In this case, the accuracy of estimation of the background flowis reduced during high-speed travel or travel on a curve in a road dueto the accuracies of sensors for detecting the vehicle speed, steeringangle and other factors and the influence of deviation between thecamera rotation center and the vehicle rotation center. Also, in theapparatus disclosed in Patent Document 1, there is a need to measure thedistance to objects existing on the road in the forward direction fromthe vehicle, obtain road information from a GPS or the like and estimatea spatial model in the spatial model estimation unit using preliminaryknowledge about spaces and models for roads, walls and so on in order toobtain the background flow. Complicated processing is thereforerequired. There is also a possibility of failure to accurately estimatethe background flow. In the apparatus disclosed in Patent Document 1,the movement of an object is detected by comparing the optical flow andthe background flow and there is, therefore, a possibility of occurrenceof a large error resulting from erroneous estimation of the backgroundflow and, hence, failure to suitably detect a movable object.

SUMMARY OF THE INVENTION

In view of the above-described circumstances, an object of the presentinvention is to provide an object detection apparatus and method capableof accurately detecting a movable object existing in a region around avehicle from images obtained through a camera mounted on the vehicle bysuitably eliminating the influence of the movement of the camera throughsimple processing, and an object detection program for making a computerexecute processing in the object detection apparatus.

To achieve the above-described object, according to a first aspect ofthe present invention, there is provided an object detection apparatuswhich detects a movable object in a region around a vehicle from a timeseries of images obtained through a camera mounted on the vehicle, theapparatus having a feature point extraction unit which extracts afeature point contained in a feature region of each of the images in thetime series, a correspondence degree computation unit which computes thedegree of correspondence for each pair of the feature points, whereinone of the feature points in the each pair is each of one or more of thefeature points extracted by the feature point extraction unit from oneof two images taken by the camera at different times, and another of thefeature points in the each pair is each of a plurality of the featurepoints extracted by the feature point extraction unit from the other ofthe two images, and a detection unit which detects the movable object onthe basis of the degree of correspondence computed by the correspondencedegree computation unit.

In the object detection apparatus according to the first aspect of thepresent invention, the feature point extraction unit extracts featurepoints contained in feature regions having, for example, characteristicshapes and colors on the image, and the correspondence degreecomputation unit computes the degrees of correspondence between onefeature point and a plurality of features points. A movable object isdetected on the basis of the degree of correspondence. Thus, detectionof a movable object only from images by simpler processing can beperformed in comparison with the case of explicitly obtaining themovement of a camera from the one-to-one relationship between images anda spatial model to detect the movable object. Also, the influence of anerror produced in computation of the movement of the camera on thedetection result is eliminated. The above-mentioned movable object is anobject having a speed relative to the ground, e.g., a moving pedestrianor a vehicle other than the vehicle using the object detectionapparatus. Thus, according to the first aspect of the present invention,the influence of the movement of the camera mounted on the vehicle issuitably eliminated by simple processing from the time series of imagesobtained through the camera to achieve accurate detection of a movableobject in a region around the vehicle.

Preferably, according to a second aspect of the present invention, theobject detection apparatus according to the first aspect of the presentinvention also includes a movement estimation unit which estimatesparameters indicating the state of movement of the camera on the basisof the degree of correspondence computed by the correspondence degreecomputation unit, and the detection unit detects the movable object onthe basis of the parameters estimated by the movement estimation unitfor indicating the state of movement, and a feature value of each of thepair having the highest degree of correspondence among each of the paircomposed of the feature point extracted from one of the two images andeach of a plurality of the feature points extracted from the other ofthe two images.

In this case, the movement of the camera due to the movement of thevehicle for example appears as an apparent motion on images in theobtained time series of images. Therefore the movement of the camera canbe estimated on the basis of the correspondence between images taken atdifferent times. According to the present invention, parametersindicating the state of movement of the camera are estimated on thebasis of the degrees of correspondence between one feature point and aplurality of feature points in two images taken at different times. Theparameters indicating the state of movement are not values explicitlyobtained for the movement of the camera but values including thetranslation and rotation of the camera in a mixed condition.

As a result, errors in the results of estimation of the camera movementare reduced in comparison with the case of estimating the movement ofthe vehicle on the basis of a one-to-one relationship and the case ofexplicitly obtaining the camera movement, and the estimation of thecamera movement and the detection of a movable object can be performedwith stability. Also, the camera movement can be estimated only fromimages without using sensors for detecting the movement of the vehicleand a spatial model. Thus, according to the second aspect of the presentinvention, the influence of the movement of the camera is suitablyeliminated by simple processing to achieve accurate detection of amovable object in a region around the vehicle.

Preferably, according a third aspect of the present invention, themovement estimation unit in the object detection apparatus according tothe second aspect of the present invention estimates the parametersindicating the state of movement of the camera on the basis of thedegree of correspondence for each of the pair of the feature pointssatisfying an epipolar constraint condition. The epipolar constraint isa condition to be satisfied in the case where some of the pair of thefeature points are in the background (the region other than movableobjects). Changes in the images produced by the movement of the cameraare evaluated by using the degree of correspondence for each of the pairof the feature points satisfying the epipolar constraint, and byassuming that the feature points are included in the background, therebyenabling the parameters indicating the movement of the camera to besuitably estimated.

Preferably, according to a fourth aspect of the present invention, thefeature point extraction unit in the object detection apparatusaccording to the first to third aspects of the present inventionextracts the feature points by using a plurality of Gabor filters havingdifferent orientations and scales, and the correspondence degreecomputation unit computes the degree of correspondence by using afeature value obtained by performing filtering with the plurality ofGabor filters on the feature points extracted by the feature pointextraction unit. In this case, the feature points can be suitablyextracted by reducing the influence of the rotation and size of themovable object on the images and other factors by the plurality of Gaborfilters having different orientations and scales. The degree ofcorrespondence is computed by using feature mounts (e.g., phasecomponents of outputs) obtained by performing filtering with the Gaborfilters on the feature points on the images. Therefore thecorrespondence between the feature points can be suitably computed.

Preferably, according to a fifth aspect of the present invention, thefeature point extraction unit in the object detection apparatusaccording to the first to third aspects of the present inventionextracts the feature points by using changes in colors on the images,and the correspondence degree computation unit computes the degree ofcorrespondence by using a feature value relating to the colors of thefeature points extracted by the feature point extraction unit. In thiscase, feature points corresponding to pedestrians or the like areordinarily different in color on the images from the backgroundincluding a road surface. Therefore the feature points can be suitablyextracted by using changes in colors. Since the degree of correspondenceis detected by using the feature value relating to the colors of thefeature points on the images, the correspondence between the featurepoints can be suitably computed.

According to the present invention, there is also provided an objectdetection method for detecting a movable object in a region around avehicle from a time series of images obtained through a camera mountedon the vehicle, the method including a feature point extraction step ofextracting a feature point contained in a feature region of each of theimages in the time series, a correspondence degree computation step ofselecting each pair of the feature points, wherein one of the featurepoints in the each pair is each of one or more of the feature pointsextracted by the feature point extraction step from one of two imagestaken by the camera at different times, and another of the featurepoints in the each pair is each of a plurality of the feature pointsextracted by the feature point extraction step from the other of the twoimages, and computing a degree of correspondence for the selected eachpair, and a detection step of detecting the movable object on the basisof the degree of correspondence computed in the correspondence degreecomputation step.

According to this object detection method, as described above withrespect to the object detection apparatus in the first aspect of thepresent invention, an movable object is detected on the basis of thedegree of correspondence and, therefore, detection of a movable objectonly from images by simpler processing can be performed in comparisonwith the case of explicitly obtaining the movement of a camera from theone-to-one relationship between images and a spatial model to detect themovable object. Also, the influence of an error produced in computationof the movement of the camera on the detection result is eliminated.Thus, according to the present invention, the influence of the movementof the camera mounted on the vehicle is suitably eliminated by simpleprocessing from the time series of images obtained through the camera toachieve accurate detection of a movable object in a region around thevehicle.

According to the present invention, there is also provided an objectdetection program for making a computer execute processing for detectinga movable object in a region around a vehicle from a time series ofimages obtained through a camera mounted on the vehicle, the programincluding the functions to make the computer execute feature pointextraction processing for extracting a feature point contained in afeature region of each of the images in the time series, correspondencedegree computation processing for selecting each pair of the featurepoints, wherein one of the feature points in the each pair is each ofone or more of the feature points extracted by the feature pointextraction processing from one of two images taken by the camera atdifferent times, and another of the feature points in the each pair iseach of a plurality of the feature points extracted by the feature pointextraction processing from the other of the two images, and forcomputing a degree of correspondence for the selected each pair, and adetection processing for detecting the movable object on the basis ofthe degree of correspondence computed by the correspondence degreecomputation processing.

This object detection program enables a computer to execute processinghaving the effects described above with respect to the object detectionapparatus according to the first aspect of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram of an object detection apparatusaccording to a first embodiment of the present invention;

FIG. 2 is a flowchart showing object detection processing in an imageprocessing unit of the object detection apparatus shown in FIG. 1;

FIG. 3 is an illustration showing an example of images obtained in theobject detection processing shown in FIG. 2 and processing forestimating parameters indicating the state of movement of a camera; and

FIG. 4 is a diagram showing an example of images representing theresults of detection of movable objects in the object detectionprocessing shown in FIG. 2.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

As shown in FIG. 1, an object detection apparatus 1 according to anembodiment of the present invention is mounted on a vehicle 2. Theobject detection apparatus 1 is attached to a front portion of thevehicle 2 and connected to a camera 3 (CCD camera or the like) whichtakes images seen in front of the vehicle 2. The object detectionapparatus 1 executes processing for detecting a movable object such as apedestrian existing in front of the vehicle 2 from images obtainedthrough the camera 3.

The object detection apparatus 1 is formed by an electronic unitconstituted by a computer which has an A/D converter circuit forconverting an input analog signal into a digital signal, an image memoryfor storing the digitized image signal and an interface circuit foraccessing data stored in the image memory (reading or writing data), andwhich performs various kinds of computation processing on images storedin the image memory (a computational processing circuit including a CPU,a memory and input and output circuits, or a microcomputer havingfunctions corresponding to those of such components). These componentsof the object detection apparatus 1 are not illustrated in detail. Anoutput signal from the camera 3 is converted into a digital signal to beinput to the computer.

The object detection apparatus 1 has, as functions thereof, an imageacquisition unit 4 which obtains images through the camera 3, a featurepoint extraction unit 5 which extracts a feature point contained in afeature region of each of images in a time series, a correspondencedegree computation unit 6 which computes the degree of correspondencebetween two images taken at different times, a movement estimation unit7 which estimates parameters indicating the state of movement of thecamera 3 on the basis of the degree of correspondence, and a detectionunit 8 which detects a movable object on the basis of the degree ofcorrespondence.

The functions of the object detection apparatus 1 are realized byexecuting in the object detection apparatus 1 a program loaded in thememory of the object detection apparatus 1 in advance. This programincludes the object detection program in accordance with the presentinvention. The program may be stored in the memory via a recordingmedium such as a CD-ROM. The program may be stored in the memory afterbeing distributed or broadcast via a network or an artificial satellitefrom an external server and received by a communication device mountedon the vehicle 2.

The image acquisition unit 4 obtains an image formed of pixel data (animage in front of the vehicle 2) through the camera 3. The output fromthe camera 3 (a video signal for a color image in this embodiment) istaken into the image acquisition unit 4 with a predetermined processingperiod. The image acquisition unit 4 performs A/D conversion of videosignals (analog signals) for the pixels of the taken image input fromthe camera 3 and stores in the image memory (not shown) digital dataobtained by the A/D conversion.

The feature point extraction unit 5 performs filtering on the imageobtained by the image acquisition unit 4 by using a plurality of Gaborfilters having predetermined orientations and scales to extract afeature point contained in a feature region. The feature region is aregion on the image having a characteristic shape and color for example.The feature region corresponds to, for example, a movable object, suchas a pedestrian or a vehicle other than the vehicle on which the objectdetection apparatus is provided, characteristic in terms of shape orcolor for example in contrast with a road surface or the skycomparatively uniform, or a region on the image corresponding to astationary object such as a tree in a region around a road, a fence or abuilding. The Gabor filter is a bandpass filter having a complex-numberoutput.

The correspondence degree computation unit 6 computes the degree ofcorrespondence for each pair of the feature points, wherein one of thefeature points in the each pair is each of one or more of the featurepoints extracted by the feature point extraction unit 5 from one of twoimages taken by the camera 3 at different times, and another of thefeature points in the each pair is each of a plurality of the featurepoints extracted by the feature point extraction unit 5 from the otherof the two images. The correspondence degree computation unit 6 computesthe degree of correspondence by using as a feature value the outputsobtained by filtering with the plurality of Gabor filters havingpredetermined orientation and scales.

The movement estimation unit 7 estimates, on the basis of the degree ofcorrespondence computed by the correspondence degree computation unit 6,parameters indicating the state of movement of the camera 3 due tofactors including the movement of the vehicle 2. In this embodiment, theparameters indicating the state of movement are a 3×3 matrix includingthe translation and rotation of the camera in mixed form.

The detection unit 8 detects a movable object on the basis of the degreeof correspondence computed by the correspondence degree computation unit6. More specifically, the detection unit 8 detects a movable object onthe basis of the parameters estimated by the movement estimation unit 7for indicating the state of movement, and the feature value of each ofthe pair having the highest degree of correspondence among each of thepair composed of the feature point extracted from one of the two imagesand each of a plurality of the feature points extracted from the otherof the two images.

The operation of the object detection apparatus (object detectionprocessing) according to this embodiment will be described withreference to the flowchart of FIG. 2. First, in STEP 1, the objectdetection apparatus 1 obtains a color image signal output from thecamera 3, converts the image signal from the analog form into thedigital form and stores the digital image signal in the image memory.

FIG. 3 shows a time series I_(t), I_(t+Δt) of images obtained throughthe camera 3. The image I_(t) is an image taken at a time t, and theimage I_(t+Δt) is an image taken at a time t+Δt. In the images I_(t) andI_(t+Δt), pedestrians T1 to T3, which are movable objects, and trees, abuilding and a standing vehicle, which are stationary objects, exist infront of the vehicle 2. The pedestrians T1 to T3 are objects to bedetected by the object detection apparatus 2. Description will be madeof the case shown in FIG. 3 by way of example.

In STEP 2, the feature point extraction unit 5 performs filtering on theimages I_(t) and I_(t+Δt) using a plurality of Gabor filters differingin orientation and scale with respect to each of the Gabor filters.

In STEP 3, the feature point extraction unit 5 extracts as a featurepoint pixels from which filtered data satisfying predeterminedconditions is obtained. More specifically, the feature point extractionunit 5 obtains, for example, a vector having as its elements outputsobtained by performing filtering with the Gabor filters (a vector havingthe number of elements corresponding to the number of Gabor filters).The feature point extraction unit 5 sets as feature point pixels forwhich the obtained vector has an absolute value equal to or larger thana predetermined value.

In STEP 4, the correspondence degree computation unit 6 computes thedegree of correspondence between the two images I_(t) and I_(t+Δt) takenat different times. More specifically, the correspondence degreecomputation unit 6 obtains a vector having as its elements phasecomponents of the outputs obtained by performing filtering using theGabor filters with respect to feature points contained in featureregions of the two images, and computes the value of correlation betweenthe obtained two vectors as the degree of correspondence P between thefeature points.

FIG. 3 shows a typical example of feature points: a feature point R onthe image I_(t), and feature points A, B, C, and D on the imageI_(t+Δt). In processing for computing the degree of correspondence P inthis case, the correspondence degree computation unit 6 computes thedegree of correspondence P for each pair of the feature points in thecombinations between the feature point R on the image I_(t), and thefeature points A, B, C, and D on the image I_(t+Δt). For example, thedegree of correspondence P (R, A)=1 between the point R and the point A,the degree of correspondence P (R, B)=0.5 between the point R and thepoint B and the degree of correspondence P (R, C)=0 between the point Rand the point C are obtained. The correspondence degree computation unit6 performs this processing with respect to all the feature points on theimage I_(t). From the consideration of the amount of computation forexample, processing by the correspondence degree computation unit 6 maybe performed by selecting a predetermined number of feature points inthe feature points extracted by the feature point extraction unit 5 andby using only the selected feature points for computation of the degreeof correspondence.

In STEP 5, the movement estimation unit 7 computes parameters indicatingthe state of movement of the camera 3 by using the degree ofcorrespondence computed in STEP 4. More specifically, the movementestimation unit 7 computes a 3×3 matrix E which maximizes the value ofan expression (1) shown below. E represents a matrix including thetranslation and rotation of the camera in mixed form.

$\begin{matrix}\lbrack {{Formula}\mspace{14mu} 1} \rbrack & \; \\{\sum\limits_{i}{\max\limits_{\underset{{p_{i}^{T}A^{- T}{EA}^{- 1}q_{j}} \leq k}{j}}{P( {p_{i},q_{j}} )}}} & (1)\end{matrix}$

In the above expression (1), p_(i) represents the ith feature point onthe image I_(t) at time t; q_(j) represents the jth feature point on theimage I_(t+Δt) at time t+_(Δ)t; A represents a 3×3 matrix including thefocal length, pixel pitch and principal points of the camera in mixedform; k is a predetermined threshold value equal to or larger than 0;and P(p_(i), q_(j)) represents the degree of correspondence betweenp_(i) and q_(j). Expression (1) means adding together the maximum valuesof P(p_(i), q_(i)) satisfying a condition expressed by an expression (2)shown below with respect to all the feature points p_(i) on the imageI_(t). It can be said that E that maximizes this expression (1) fitswell to the change in the image. That is, if a predetermined E is given,then only q_(j) satisfying the following expression (2):[Formula 2]p _(i) ^(T) A ^(−T) EA ⁻¹ q _(i) ≦k  (2)is an object for “max” in expression (1) (an object to be computed inexpression 1) with respect to predetermined p_(i). If E is given, and ifp_(i) is assumed to be a feature point included in the background (theregion other than the movable objects), there is a restriction on thelocation of the image at time t+Δt from the image p_(i) at time t. Thecondition shown by expression (2) represents an epipolar constraintcorresponding to this restriction. The value k is ideally 0 but acertain value is taken by considering an error or the like since thereis the influence of the error or the like. By selecting and addingtogether objects q_(j) having the maximum degree of correspondence withrespect to all p_(j), the degree of fitting of the assumed E to changein the actual image is expressed in numeric form. E that maximizes thisdegree of fitting is obtained to inexplicitly estimate the movement ofthe camera. E that maximizes this degree of fitting can be obtained in asearching manner by performing ordinary optimization processing.

In STEP 6, the detection unit 8 obtains the each of the pair of thefeature points having the highest degree of correspondence among thosecomputed by the correspondence degree computation unit 6 with respect toall the feature points on the image I_(t).

In STEP 7, a value X shown by the following equation (3) is computed byusing the feature value that the pair of the feature points obtained inSTEP 6 has and the parameters E estimated in STEP 5 for indicating thestate of movement.[Formula 3]X=p _(i) ^(T) A ^(−T) EA ⁻¹ q _(j)  (3)

That is, expression (2) is satisfied with respect to the background.Therefore a non-background portion (a movable object) is identified ifexpression (2) is not satisfied. The detection unit 8 then determinesthat the points having a value X equal to or larger than a predeterminedthreshold value m (m>k) belongs to movable objects. Points belonging tomovable objects are thereby extracted, as indicated by white regions(other than the region indicated by a grid of dots) in FIG. 4 by way ofexample.

In STEP 8, the detection unit 8 detects movable objects by removingnoise through certain processing such as clustering on the pointsdetermined as belonging to movable objects in STEP 7. As a result,movable objects T1 to T3 illustrated in FIG. 4 are thus detected.

Through the above-described processing, movable objects T1 to T3 can bedetected with accuracy from the time series I_(t), I_(t+Δt) of imagesobtained through the camera 3.

In the above-described embodiment, the feature point extraction unit 5extracts feature points by using Gabor filters and the correspondencedegree computation unit 6 computes the degree of correspondence by usingthe feature value obtained by performing Gabor filtering on theextracted feature points. However, another embodiment is conceivable inwhich the feature point extraction unit 5 extracts feature points byusing changes in colors on the image and the correspondence degreecomputation unit 6 computes the degree of correspondence using a featurevalue with respect to the extracted feature point colors.

1. An object detection apparatus which detects a movable object in aregion around a vehicle from a time series of images including at leasta first and second image obtained through a camera mounted on thevehicle, the apparatus comprising: a feature point extraction unit whichextracts a first set of feature point contained in a feature region ofthe first image and a second set of feature point contained in a featureregion of the second image; a correspondence degree computation unitwhich: creates a combination set of pairs of feature points by pairingeach feature point from the first set with each feature point of thesecond set, computes a degree of correspondence for each pair of thefeature points in the combination set, and reduces the combination setto a corresponding pairs set that includes the pairs from thecombination set having the highest computed degree of correspondence foreach feature point of the first set; and a detection unit which detectsthe movable object on the basis of the degree of correspondence computedby the correspondence degree computation unit.
 2. The object detectionapparatus according to claim 1, further comprising a movement estimationunit which estimates parameters indicating the state of movement of thecamera on the basis of the degree of correspondence computed by thecorrespondence degree computation unit, wherein the detection unitdetects the movable object on the basis of the parameters estimated bythe movement estimation unit for indicating the state of movement, and afeature value of each of the corresponding pairs of the set ofcorresponding pairs.
 3. The object detection apparatus according toclaim 2, wherein the movement estimation unit estimates the parametersindicating the state of movement of the camera on the basis of thedegree of correspondence for each of the corresponding pairs of the setof corresponding pairs satisfying an epipolar constraint condition. 4.The object detection apparatus according to claim 2, wherein the featurepoint extraction unit extracts the feature points by using a pluralityof Gabor filters having different orientations and scales, and thecorrespondence degree computation unit computes the degree ofcorrespondence by using a feature value obtained by performing filteringwith the plurality of Gabor filters on the feature points extracted bythe feature point extraction unit.
 5. The object detection apparatusaccording to claim 2, wherein the feature point extraction unit extractsthe feature points by using changes in colors on the images, and thecorrespondence degree computation unit computes the degree ofcorrespondence by using a feature value relating to the colors of thefeature points extracted by the feature point extraction unit.
 6. Anobject detection method for detecting a movable object in a regionaround a vehicle from a time series of images including at least a firstand second image obtained through a camera mounted on the vehicle, themethod comprising: a feature point extraction step of extracting a firstset of feature points contained in a feature region of the first imageand a second set of feature point contained in a feature region of thesecond image; a correspondence degree computation step of: creating acombination set of pairs of feature points by pairing each feature pointfrom the first set with each feature point of the second set, computinga degree of correspondence for each pair of feature points in thecombination set, and reducing the combination set to a correspondingpairs set that includes the pairs from the combination set having thehighest computed degree of correspondence for each feature point of thefirst set; and a detection step of detecting the movable object on thebasis of the degree of correspondence computed in the correspondencedegree computation step.